Resonance Compensation of Extended State Observer Improved Based on Inertia Ratio

This paper proposes an improved model compensation method based on the inertia ratio. The method enhances the Extended State Observer (ESO) by introducing an adjustable inertia ratio, broadening its application scope. The elastic system is modeled by combining a rigid model with an elastic disturbance model, illustrating how controller compensation interacts with the elastic system and how the system's inertia ratio limits the controller's applicability. Based on ESO's application in elastic systems, this paper integrates the inertia ratio concept with the extended observer structure to achieve combined feedback. Simulation results demonstrate the proposed method's excellent disturbance and resonance suppression capabilities.

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Publication Details

Published
2025-03-21
DOI
https://doi.org/10.1109/icaace65325.2025.11019753
Citations
1
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
1.21

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article

Resonance Compensation of Extended State Observer Improved Based on Inertia Ratio

Jieying Lu, Guoxin Zhang, Cheng Wen
1 citations
Iterative Learning Control Systems
1.21
article

Resonance Compensation of Extended State Observer Improved Based on Inertia Ratio

Jieying Lu, Guoxin Zhang, Cheng Wen
article en
1 citations

Abstract

This paper proposes an improved model compensation method based on the inertia ratio. The method enhances the Extended State Observer (ESO) by introducing an adjustable inertia ratio, broadening its application scope. The elastic system is modeled by combining a rigid model with an elastic disturbance model, illustrating how controller compensation interacts with the elastic system and how the system's inertia ratio limits the controller's applicability. Based on ESO's application in elastic systems, this paper integrates the inertia ratio concept with the extended observer structure to achieve combined feedback. Simulation results demonstrate the proposed method's excellent disturbance and resonance suppression capabilities.

South China University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 21%
Iterative Learning Control Systems
1.21
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